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----
-license: apache-2.0
-language:
-- zh
-- en
-pipeline_tag: text-generation
-library_name: transformers
----
-<div align="center">
-<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
-</div>
-<p align="center">
-<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
-<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a>
-</p>
-<p align="center">
-👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
-</p>
-## What's New
-* [2025-06-05] 🚀🚀🚀 We have open-sourced **MiniCPM4-Survey**, a model built upon MiniCPM4-8B that is capable of generating trustworthy, long-form survey papers while maintaining competitive performance relative to significantly larger models.
-## MiniCPM4 Series
-MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
-- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
-- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
-- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
-- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
-- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
-- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
-- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
-- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
-- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. (**<-- you are here**)
-- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
-## Overview
-**MiniCPM4-Survey** is an open-source LLM agent model jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China and [ModelBest](https://modelbest.cn/en). Built on [MiniCPM4](https://github.com/OpenBMB/MiniCPM4) with 8 billion parameters, it accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
-Key features include:
-- **Plan-Retrieve-Write Survey Generation Framework** — We propose a multi-agent generation framework, which operates through three core stages: planning (defining the overall structure of the survey), retrieval (generating appropriate retrieval keywords), and writing (synthesizing the retrieved information to generate coherent section-level content).
-- **High-Quality Dataset Construction** — We gather and process lots of expert-written survey papers to construct a high-quality training dataset. Meanwhile, we collect a large number of research papers to build a retrieval database.
-- **Multi-Aspect Reward Design** — We carefully design a reward system with three aspects (structure, content, and citations) to evaluate the quality of the surveys, which is used as the reward function in the RL training stage.
-- **Multi-Step RL Training Strategy** — We propose a *Context Manager* to ensure retention of essential information while facilitating efficient reasoning, and we construct *Parallel Environment* to maintain efficient RL training cycles.
-## Quick Start
-### Download the model
-Download [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey) from Hugging Face and place it in `model/MiniCPM4-Survey`.
-We recommend using [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light) as the embedding model, which can be downloaded from Hugging Face and placed in `model/MiniCPM-Embedding-Light`.
-### Perpare the environment
-You can download the [paper data](https://www.kaggle.com/datasets/Cornell-University/arxiv) from Kaggle, then extract it. You can run `python data_process.py` to process the data and generate the retrieval database. Then you can run `python build_index.py` to build the retrieval database.
-```
-cd ./code
-curl -L -o ~/Downloads/arxiv.zip\
-   https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
-unzip ~/Downloads/arxiv.zip -d .
-mkdir data
-python ./src/preprocess/data_process.py
-mkdir index
-python ./src/preprocess/build_index.py
-```
-### Model Inference
-You can run the following command to build the retrieval environment and start the inference:
-```bash
-cd ./code
-python ./src/retriever.py
-bash ./scripts/run.sh
-```
-If you want to run with the frontend, you can run the following command:
-```bash
-cd ./code
-python ./src/retriever.py
-bash ./scripts/run_with_frontend.sh
-cd frontend/minicpm4-survey
-npm install
-npm run dev
-```
-Then you can visit `http://localhost:5173` in your browser to use the model.
-## Performance Evaluation
-| Method                                      | Relevance | Coverage | Depth | Novelty | Avg.  | Fact Score |
-|---------------------------------------------|-----------|----------|-------|---------|-------|------------|
-| Naive RAG (driven by G2FT)                  | 3.25      | 2.95     | 3.35  | 2.60    | 3.04  | 43.68      |
-| AutoSurvey (driven by G2FT)                 | 3.10      | 3.25     | 3.15  | **3.15**| 3.16  | 46.56      |
-| Webthinker (driven by WTR1-7B)              | 3.30      | 3.00     | 2.75  | 2.50    | 2.89  | --         |
-| Webthinker (driven by QwQ-32B)              | 3.40      | 3.30     | 3.30  | 2.50    | 3.13  | --         |
-| OpenAI Deep Research (driven by GPT-4o)     | 3.50      |**3.95**  | 3.55  | 3.00    | **3.50**  | --         |
-| MiniCPM4-Survey                            | 3.45      | 3.70     | **3.85** | 3.00    | **3.50**  | **68.73**  |
-| &nbsp;&nbsp;&nbsp;*w/o* RL                  | **3.55**  | 3.35     | 3.30  | 2.25    | 3.11  | 50.24      |
-*Performance comparison of the survey generation systems. "G2FT" stands for Gemini-2.0-Flash-Thinking, and "WTR1-7B" denotes Webthinker-R1-7B. FactScore evaluation was omitted for Webthinker, as it does not include citation functionality, and for OpenAI Deep Research, which does not provide citations when exporting the results.*
-## Statement
-- As a language model, MiniCPM generates content by learning from a vast amount of text.
-- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
-- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
-- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
-## LICENSE
-- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
-## Citation
-- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
-```bibtex
-@article{minicpm4,
-  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
-  author={MiniCPM Team},
-  year={2025}
-}
-```
-# 中文
-## News
-* [2025-06-05] 🚀🚀🚀我们开源了基于MiniCPM4-8B构建的MiniCPM4-Survey，能够生成可信的长篇调查报告，性能比肩更大模型。
-## 概览
-MiniCPM4-Survey是由[THUNLP](https://nlp.csai.tsinghua.edu.cn)、中国人民大学和[ModelBest](https://modelbest.cn)联合开发的开源大语言模型智能体。它基于[MiniCPM4](https://github.com/OpenBMB/MiniCPM4) 80亿参数基座模型，接受用户质量作为输入，自主生成可信的长篇综述论文。
-主要特性包括：
-- 计划-检索-写作生成框架 — 我们提出了一个多智能体生成框架，包含三个核心阶段：计划（定义综述的整体结构）、检索（生成合适的检索关键词）和写作（利用检索到的信息，生成连贯的段落）。
-- 高质量数据集构建——我们收集并处理大量人类专家写作的综述论文，构建高质量训练集。同时，我们收集大量研究论文，构建检索数据库。
-- 多方面奖励设计 — 我们精心设计了包含结构、内容和引用的奖励，用于评估综述的质量，在强化学习训练阶段作奖励函数。
-- 多步强化学习训练策略 — 我们提出了一个上下文管理器，以确保在促进有效推理的同时保留必要的信息，并构建了并行环境，维持强化学习训练高效。
-## 使用
-### 下载模型
-从 Hugging Face 下载[MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey)并将其放在model/MiniCPM4-Survey中。
-我们建议使用[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)作为表���模型，放在model/MiniCPM-Embedding-Light中。
-### 准备环境
-从 Kaggle 下载论文数据，然后解压。运行`python data_process.py`，处理数据并生成检索数据库。然后运行`python build_index.py`，构建检索数据库。
-``` bash
-cd ./code
-curl -L -o ~/Downloads/arxiv.zip\
-   https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
-unzip ~/Downloads/arxiv.zip -d .
-mkdir data
-python ./src/preprocess/data_process.py
-mkdir index
-python ./src/preprocess/build_index.py
-```
-### 模型推理
-运行以下命令来构建检索环境并开始推理：
-``` bash
-cd ./code
-python ./src/retriever.py
-bash ./scripts/run.sh
-```
-如果您想使用前端运行，可以运行以下命令：
-``` bash
-cd ./code
-python ./src/retriever.py
-bash ./scripts/run_with_frontend.sh
-cd frontend/minicpm4-survey
-npm install
-npm run dev
-```
-然后你可以在浏览器中访问`http://localhost:5173`使用。
-## 性能
-| Method                                      | Relevance | Coverage | Depth | Novelty | Avg.  | Fact Score |
-|---------------------------------------------|-----------|----------|-------|---------|-------|------------|
-| Naive RAG (driven by G2FT)                  | 3.25      | 2.95     | 3.35  | 2.60    | 3.04  | 43.68      |
-| AutoSurvey (driven by G2FT)                 | 3.10      | 3.25     | 3.15  | **3.15**| 3.16  | 46.56      |
-| Webthinker (driven by WTR1-7B)              | 3.30      | 3.00     | 2.75  | 2.50    | 2.89  | --         |
-| Webthinker (driven by QwQ-32B)              | 3.40      | 3.30     | 3.30  | 2.50    | 3.13  | --         |
-| OpenAI Deep Research (driven by GPT-4o)     | 3.50      |**3.95**  | 3.55  | 3.00    | **3.50**  | --         |
-| MiniCPM4-Survey                            | 3.45      | 3.70     | **3.85** | 3.00    | **3.50**  | **68.73**  |
-| &nbsp;&nbsp;&nbsp;*w/o* RL                  | **3.55**  | 3.35     | 3.30  | 2.25    | 3.11  | 50.24      |
-*GPT-4o对综述生成系统的性能比较。“G2FT”代表Gemini-2.0-Flash-Thinking，“WTR1-7B”代表Webthinker-R1-7B。由于Webthinker不包括引用功能，OpenAI Deep Research在导出结果时不提供引用，因此省略了对它们的FactScore评估。我们的技术报告中包含评测的详细信息。*

+---
+language:
+- zh
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+---
+<div align="center">
+<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
+</div>
+<p align="center">
+<a href="https://github.com/OpenBMB/MiniCPM/\" target="_blank">GitHub Repo</a> |
+<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a> |
+<a href="https://huggingface.co/papers/2506.07900" target="_blank">Paper</a>
+</p>
+<p align="center">
+👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
+</p>
+This repository contains the model described in the paper [MiniCPM4: Ultra-Efficient LLMs on End Devices](https://huggingface.co/papers/2506.07900).
+## What's New
+* [2025-06-05] 🚀🚀🚀 We have open-sourced **MiniCPM4-Survey**, a model built upon MiniCPM4-8B that is capable of generating trustworthy, long-form survey papers while maintaining competitive performance relative to significantly larger models.
+## MiniCPM4 Series
+MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
+- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
+- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
+- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
+- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
+- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
+- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
+- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
+- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
+- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. (**<-- you are here**)
+- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
+## Overview
+**MiniCPM4-Survey** is an open-source LLM agent model jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China and [ModelBest](https://modelbest.cn/en). Built on [MiniCPM4](https://github.com/OpenBMB/MiniCPM4) with 8 billion parameters, it accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
+Key features include:
+- **Plan-Retrieve-Write Survey Generation Framework** — We propose a multi-agent generation framework, which operates through three core stages: planning (defining the overall structure of the survey), retrieval (generating appropriate retrieval keywords), and writing (synthesizing the retrieved information to generate coherent section-level content).
+- **High-Quality Dataset Construction** — We gather and process lots of expert-written survey papers to construct a high-quality training dataset. Meanwhile, we collect a large number of research papers to build a retrieval database.
+- **Multi-Aspect Reward Design** — We carefully design a reward system with three aspects (structure, content, and citations) to evaluate the quality of the surveys, which is used as the reward function in the RL training stage.
+- **Multi-Step RL Training Strategy** — We propose a *Context Manager* to ensure retention of essential information while facilitating efficient reasoning, and we construct *Parallel Environment* to maintain efficient RL training cycles.
+## Quick Start
+### Download the model
+Download [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey) from Hugging Face and place it in `model/MiniCPM4-Survey`.
+We recommend using [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light) as the embedding model, which can be downloaded from Hugging Face and placed in `model/MiniCPM-Embedding-Light`.
+### Perpare the environment
+You can download the [paper data](https://www.kaggle.com/datasets/Cornell-University/arxiv) from Kaggle, then extract it. You can run `python data_process.py` to process the data and generate the retrieval database. Then you can run `python build_index.py` to build the retrieval database.
+```
+cd ./code
+curl -L -o ~/Downloads/arxiv.zip\
+   https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
+unzip ~/Downloads/arxiv.zip -d .
+mkdir data
+python ./src/preprocess/data_process.py
+mkdir index
+python ./src/preprocess/build_index.py
+```
+### Model Inference
+You can run the following command to build the retrieval environment and start the inference:
+```bash
+cd ./code
+python ./src/retriever.py
+bash ./scripts/run.sh
+```
+If you want to run with the frontend, you can run the following command:
+```bash
+cd ./code
+python ./src/retriever.py
+bash ./scripts/run_with_frontend.sh
+cd frontend/minicpm4-survey
+npm install
+npm run dev
+```
+Then you can visit `http://localhost:5173` in your browser to use the model.
+## Performance Evaluation
+| Method                                      | Relevance | Coverage | Depth | Novelty | Avg.  | Fact Score |
+|---------------------------------------------|-----------|----------|-------|---------|-------|------------|
+| Naive RAG (driven by G2FT)                  | 3.25      | 2.95     | 3.35  | 2.60    | 3.04  | 43.68      |
+| AutoSurvey (driven by G2FT)                 | 3.10      | 3.25     | 3.15  | **3.15**| 3.16  | 46.56      |
+| Webthinker (driven by WTR1-7B)              | 3.30      | 3.00     | 2.75  | 2.50    | 2.89  | --         |
+| Webthinker (driven by QwQ-32B)              | 3.40      | 3.30     | 3.30  | 2.50    | 3.13  | --         |
+| OpenAI Deep Research (driven by GPT-4o)     | 3.50      |**3.95**  | 3.55  | 3.00    | **3.50**  | --         |
+| MiniCPM4-Survey                            | 3.45      | 3.70     | **3.85** | 3.00    | **3.50**  | **68.73**  |
+| &nbsp;&nbsp;&nbsp;*w/o* RL                  | **3.55**  | 3.35     | 3.30  | 2.25    | 3.11  | 50.24      |
+*Performance comparison of the survey generation systems. "G2FT" stands for Gemini-2.0-Flash-Thinking, and "WTR1-7B" denotes Webthinker-R1-7B. FactScore evaluation was omitted for Webthinker, as it does not include citation functionality, and for OpenAI Deep Research, which does not provide citations when exporting the results.*
+## Statement
+- As a language model, MiniCPM generates content by learning from a vast amount of text.
+- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
+- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
+- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
+## LICENSE
+- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
+## Citation
+- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
+```bibtex
+@article{minicpm4,
+  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
+  author={MiniCPM Team},
+  year={2025}
+}
+```
+# 中文
+## News
+* [2025-06-05] 🚀🚀🚀我们开源了基于MiniCPM4-8B构建的MiniCPM4-Survey，能够生成可信的长篇调查报告，性能比肩更大模型。
+## 概览
+MiniCPM4-Survey是由[THUNLP](https://nlp.csai.tsinghua.edu.cn)、中国人民大学和[ModelBest](https://modelbest.cn)联合开发的开源大语言模型智能体。它基于[MiniCPM4](https://github.com/OpenBMB/MiniCPM4) 80亿参数基座模型，接受用户质量作为输入，自主生成可信的长篇综述论文。
+主要特性包括：
+- 计划-检索-写作生成框架 — 我们提出了一个多智能体生成框架，包含三个核心阶段：计划（定义综述的整体结构）、检索（生成合适的检索关键词）和写作（利用检索到的信息，生成连贯的段落）。
+- 高质量数据集构建——我们收集并处理大量人类专家写作的综述论文，构建高质量训练集。同时，我们收集大量研究论文，构建检索数据库。
+- 多方面奖励设计 — 我们精心设计了包含结构、内容和引用的奖励，用于评估综述的质量，在强化学习训练阶段作奖励函数。
+- 多步强化学习训练策略 — 我们提出了一个上下文管理器，以确保在促进有效推理的同时保留必要的信息，并构建了并行环境，维持强化学习训练高效。
+## 使用
+### 下载模型
+从 Hugging Face 下载[MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey)并将其放在model/MiniCPM4-Survey中。
+我们建议使用[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)作为表征模型，放在model/MiniCPM-Embedding-Light中。
+### 准备环境
+从 Kaggle 下载论文数据，然后解压。运行`python data_process.py`，处理数据并生成检索数据库。然后运行`python build_index.py`，构建检索数据库。
+``` bash
+cd ./code
+curl -L -o ~/Downloads/arxiv.zip\
+   https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
+unzip ~/Downloads/arxiv.zip -d .
+mkdir data
+python ./src/preprocess/data_process.py
+mkdir index
+python ./src/preprocess/build_index.py
+```
+### 模型推理
+运行以下命令来构建检索环境并开始推理：
+``` bash
+cd ./code
+python ./src/retriever.py
+bash ./scripts/run.sh
+```
+如果您想使用前端运行，可以运行以下命令：
+``` bash
+cd ./code
+python ./src/retriever.py
+bash ./scripts/run_with_frontend.sh
+cd frontend/minicpm4-survey
+npm install
+npm run dev
+```
+然后你可以在浏览器中访问`http://localhost:5173`使用。
+## 性能
+| Method                                      | Relevance | Coverage | Depth | Novelty | Avg.  | Fact Score |
+|---------------------------------------------|-----------|----------|-------|---------|-------|------------|
+| Naive RAG (driven by G2FT)                  | 3.25      | 2.95     | 3.35  | 2.60    | 3.04  | 43.68      |
+| AutoSurvey (driven by G2FT)                 | 3.10      | 3.25     | 3.15  | **3.15**| 3.16  | 46.56      |
+| Webthinker (driven by WTR1-7B)              | 3.30      | 3.00     | 2.75  | 2.50    | 2.89  | --         |
+| Webthinker (driven by QwQ-32B)              | 3.40      | 3.30     | 3.30  | 2.50    | 3.13  | --         |
+| OpenAI Deep Research (driven by GPT-4o)     | 3.50      |**3.95**  | 3.55  | 3.00    | **3.50**  | --         |
+| MiniCPM4-Survey                            | 3.45      | 3.70     | **3.85** | 3.00    | **3.50**  | **68.73**  |
+| &nbsp;&nbsp;&nbsp;*w/o* RL                  | **3.55**  | 3.35     | 3.30  | 2.25    | 3.11  | 50.24      |
+*GPT-4o对综述生成系统的性能比较。“G2FT”代表Gemini-2.0-Flash-Thinking，“WTR1-7B”代表Webthinker-R1-7B。由于Webthinker不包括引用功能，OpenAI Deep Research在导出结果时不提供引用，因此省略了对它们的FactScore评估。我们的技术报告中包含评测的详细信息。*
+# File information
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